Reduction of uncertainty in projection of growth and yield of Hyrcanian trees in Jabowa-4 model by applying artificial neural network (Case study: Kheyroud forest- Nowshahr of Iran)

Document Type : Research paper


1 Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran

2 Department of Agricultural Machinery Engineering, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran


Gap models have a long history in assessing the potential effects of climate change on forest structure and composition. In Hyrcanian forests, there is a lack of efficient models for predicting growth and yield. Therefore, to fill this gap in this study, we tried to use the Jabowa-4 model, which has the ability to predict forest dynamics by considering climatic factors. To apply the model in Hyrcanian forests, the main species selected and parameterized in different modes using experimental models. After the simulation process over 90 years, the values ​​related to the observed and predicted BA, correlation coefficient and RMSE calculated and the species response to climate change evaluated. The results of this simulation show that climate change can have a negative impact on growth and yield by reducing rainfall and creating drought conditions in the studied forests. Due to these changes, the percentage of more resistant speciessuch as Oak increases.On the other hand, the results of this study showed that Jabowa-4 is effective in providing forest performance predictions. However, it has a weak ability to explain the amount and height of trees in Hyrcanian forests.

Keywords: Climate change, Forest dynamics, Forest management, Growth and yield model,

Graphical Abstract

Reduction of uncertainty in projection of growth and yield of Hyrcanian trees in Jabowa-4 model by applying artificial neural network (Case study: Kheyroud forest- Nowshahr of Iran)


  • The present study conducted due to the lack of efficient growth and yield models in important forests of northern Iran.
  • The effect of environmental factors such as climate change, which until now were considered static, was examined.
  • The use of gap models for the first time in these areas in order to depict the dynamics and succession of forest stands examined.
  • The use of artificial networks to reduce growth and yield prediction uncertainty was investigated.


Main Subjects

Ashraf, M.I., Bourque, C.P.A., MacLean, D.A., Erdle, T., Meng, F.R., 2012. Using JABOWA-3 for forest growth and yield predictions under diverse forest conditions of Nova Scotia, Canada. For. Chron., 88(6), 708-721.
Ashraf, M.I., Meng, F.R., Bourque, C.P.A., MacLean, D.A., 2015. A novel modelling approach for predicting forest growth and yield under climate change. PloS One, 10(7), e0132066.
Bayat, M., Pukkala, T., Namiranian, M., Zobeiri, M., 2013. Productivity and optimal management of the uneven-aged hardwood forests of Hyrcania. Eur. J. For. Res., 132(5), 851-864.
Botkin, D.B., 1993. Forest dynamics: an ecological model. Oxford University Press on Demand. ISBN: 0195065557
Botkin, D.B., Janak, J.F., Wallis, J.R., 1972. Rationale, limitations, and assumptions of a northeastern forest growth simulator. IBM J. Res. Dev., 16(2), 101-116.
Boote, K.J., Jones, J.W., Hoogenboom, G., White, J.W., 2010. The role of crop systems simulation in agriculture and environment. Int. J. Agric. Environ. Inf. Syst., 1(1), 41-54.
Engler, R., Randin, C.F., Thuiller, W., Dullinger, S., Zimmermann, N.E., Araújo, M.B., Pearman, P.B., Le Lay, G., Piedallu, C., Albert, C.H., Choler, P., 2011. 21st century climate change threatens mountain flora unequally across Europe. Glob. Change Biol., 17(7), 2330-2341.
Fang, J., Lechowicz, M.J., 2006. Climatic limits for the present distribution of beech (Fagus L.) species in the world. J. Biogeogr., 33(10), 1804-1819.
Gál, J. Bella, I.E., 1995. Error assessment for a provincial timber inventory. For. Chron., 71(5), 627-632.
Iverson, L.R., Prasad, A.M., 2002. Potential redistribution of tree species habitat under five climate change scenarios in the eastern US. Forest Ecol. Manag., 155(1-3), 205-222.
Kramer, K., Degen, B., Buschbom, J., Hickler, T., Thuiller, W., Sykes, M.T., de Winter, W., 2010. Modelling exploration of the future of European beech (Fagus sylvatica L.) under climate change—range, abundance, genetic diversity and adaptive response. Forest Ecol. Manag., 259(11), 2213-2222.
Lee, K.H., Goulding, C.J., 2002. Practicality of 3P sampling with accurate dendrometry for the pre-harvest inventory of plantations. New Zealand J. For. Sci., 32(2), 279-296.
Matsui, T., Takahashi, K., Tanaka, N., Hijioka, Y., Horikawa, M., Yagihashi, T., Harasawa, H., 2009. Evaluation of habitat sustainability and vulnerability for beech (Fagus crenata) forests under 110 hypothetical climatic change scenarios in Japan. Appl. Veg. Sci., 12(3), 328-339.
Mohren, G.M.J., Bartelink, H.H., Jansen, J.J., 1994. Contrasts between biologically-based process models and management-oriented growth and yield models. For. Ecol. Manag., 69, 1-5.
Monserud, R.A., 2003. Evaluating forest models in a sustainable forest management context. For. Biometr. Model. Info. Sci., 1(1), 35-47.
Park, Y.R., Murray, T.J., Chen, C., 1996. Predicting sun spots using a layered perceptron neural network. IEEE Trans. Neural Netw., 7(2), 501-505.
Peng, C., 2000. Growth and yield models for uneven-aged stands: past, present and future. For. Ecol. Manag., 132(2-3), 259-279.
Pojar, J., Klinka, K., Meidinger, D.V., 1987. Biogeoclimatic ecosystem classification in British Columbia. For. Ecol. Manag., 22(1-2), 119-154.
Pukkala, T., Lähde, E., Laiho, O., 2009. Growth and yield models for uneven-sized forest stands in Finland. For. Ecol. Manag., 258(3), 207-216.
Raymond, P., Bédard, S., Roy, V., Larouche, C., Tremblay, S., 2009. The irregular shelterwood system: review, classification, and potential application to forests affected by partial disturbances. J. For., 107(8), 405-413.
Ringvall, A., Kruys, N., 2005. Sampling of sparse species with probability proportional to prediction. Environ. Monit. Assess., 104(1), 131-146.
Robinson, A.P., Monserud, R.A., 2003. Criteria for comparing the adaptability of forest growth models. Forest Ecology and Management, 172(1), 53-67.
Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1985. Learning internal representations by error propagation. California Univ San Diego La Jolla Inst for Cogn. Sci.
Trasobares, A., Zingg, A., Walthert, L. Bigler, C., 2016. A climate-sensitive empirical growth and yield model for forest management planning of even-aged beech stands. Eur. J. For. Res., 135(2), 263-282.